Challenge 9

Author

Eva Dykaar

Code

# Loading in packages

library(tidyverse)
library(ggplot2)
library(gt)
library(kableExtra)

Code

# Load in data set

names <- read_csv(here::here("Week 9", "StateNames_A.csv"))

Interactive Preview of Data Set

Code

DT::datatable(names)

Make a summary table of the number of babies named “Allison” for each state and the sex of the baby. Specifically, the table should have the following qualities:

each state should be its own row
and each sex should have its own column
if there were no babies born for that combination of state & sex there should be a 0 (not an NA)

Code

# Renaming column 
names <- names |>
  rename(Sex = Gender)

Code

#Source for how to make the table striped: https://apreshill.github.io/data-vis-labs-2018/05-tables.html
# Revised %in% to == because its a single value

names |>
  filter(Name == "Allison") |>
  group_by(State, Sex) |>
  summarize(Count = sum(Count), .groups = "drop") |>
  pivot_wider(names_from = Sex, values_from = Count, values_fill = 0) |>
  knitr::kable(format = "html", 
               digits = 3, 
               col.names = 
                 c("State", 
                   "Female", 
                   "Male"),
               caption = "Number of People Named Allison by Sex and State") |>
  kable_styling(font_size = 15, bootstrap_options = "striped")

Number of People Named Allison by Sex and State
State	Female	Male
AK	232	0
AL	1535	0
AR	1198	0
AZ	1880	0
CA	12413	0
CO	1594	0
CT	1099	0
DC	321	0
DE	294	0
FL	4455	0
GA	3257	0
HI	183	0
IA	1477	0
ID	451	0
IL	5110	0
IN	3067	0
KS	1283	0
KY	1905	20
LA	1209	0
MA	2218	0
MD	2229	0
ME	340	0
MI	4014	0
MN	2374	0
MO	2882	0
MS	817	0
MT	226	0
NC	3435	0
ND	285	0
NE	807	0
NH	412	0
NJ	3052	0
NM	399	0
NV	729	0
NY	5747	0
OH	5487	0
OK	1421	0
OR	1186	0
PA	4307	0
RI	306	0
SC	1228	0
SD	376	0
TN	2488	0
TX	10192	0
UT	1125	0
VA	3220	0
VT	135	0
WA	1956	0
WI	2367	0
WV	813	0
WY	142	0

You should have seen in the table above that “Allison” is a name given overwhelmingly to babies assigned “female” at birth. So, create a new dataset named allison_f which contains only the babies assigned Female at birth.

Code

# Revised %in% to == because its a single value

allison_f <- names |>
  filter(Name == "Allison", Sex == "F")

Make a visualization showing how the popularity of the name “Allison” has changed over the years. To be clear, each year should have one observation–the total number of Allisons born that year.

Code

#Revised so the graph is a line graph an not a scatter plot and took out the y axis so people do not have to tilt their heads to read this graph

allison_f |>
  group_by(Year) |>
  summarize(Count = sum(Count), .groups = "drop") |>
  ggplot(mapping = aes(x = Year, y = Count)) +
  geom_line(color = "cadetblue") +
  scale_x_continuous(breaks = c("1997" : "2014")) +
  labs(x = "Year", y = "", 
       title = "Total Number of Female Allisons Born Each Year") +
  theme(text = element_text(colour = "navy"))

Fit a linear model with the year as the explanatory variable, and the number of Allisons as the response. Similar to #3, each year should have one observation–the total number of Allisons born that year.

Code

allison_f |>
  group_by(Year) |>
  summarize(Count = sum(Count), .groups = "drop") |>
  ggplot(mapping = aes(x = Year, y = Count)) +
  geom_point(color = "darkorange") +
  geom_smooth(method = "lm") +
  scale_x_continuous(breaks = c("1997" : "2014")) +
  labs(x = "Year", y = "Count",
       title = "Total Number of Female Allisons Born Each Year")

Write out the estimated regression equation.

Code

allison_f_lm <- allison_f |>
  group_by(Year) |>
  summarize(Count = sum(Count), .groups = "drop") |>
  lm(Count ~ Year, data = _)

allison_f_lm


Call:
lm(formula = Count ~ Year, data = summarize(group_by(allison_f, 
    Year), Count = sum(Count), .groups = "drop"))

Coefficients:
(Intercept)         Year  
   209689.8       -101.5

The regression equation is: Yhat = 209689.8 - 101.5 * X

Plot the residuals of the model, that is, the actual values minus the predicted values. Comment on the residuals - do you see any patterns?

Code

allison_f_lm |> 
  broom::augment() |> 
  ggplot(mapping = aes(y = .resid, x = .fitted)) +
  geom_point(color = "navy") +
  labs(x = "Fitted Values", y = "Residual Values", title = "Plotted Residuals vs Fitted values")

I see two patterns is the residuals, one is a “U” shape on the left and there is another grouping of residuals on the right.

What do you conclude from this model? Is my name not cool anymore?

From this model I have concluded that the amount of females named Allison has decreased since 1997 however there are a couple years, 2008 and 2009, where the number of Allisons increased. I would say that the popularity of the name Allison has decreased but it is still a popular name.

Narrow the A name data set (downloaded previously) down to only male-assigned babies named “Allan”, “Alan”, or “Allen”. Make a plot comparing the popularity of these names over time.

Code

# Source for changing text size: https://statisticsglobe.com/change-font-size-of-ggplot2-plot-in-r-axis-text-main-title-legend


allen <- names |>
  filter(Name %in% c("Allan", "Alan", "Allen"), Sex == "M")

allen |>
  group_by(Year, Name) |>
  summarize(Count = sum(Count), .groups = "drop") |>
  ggplot(mapping = aes(x = Year, y = Count)) +
  geom_line(color = "maroon") +
  facet_wrap(~ Name) +
  theme(text = element_text(size = 15))

In California, Allan’s spelling of his name is the least common of the three but perhaps it’s not such an unusual name for his home state of Pennsylvania. Compute the total number of babies born with each spelling of “Allan” in the year 2000, in Pennsylvania and in California. Specifically, the table should have the following qualities:

each spelling should be its own column
each state should have its own row
a 0 (not an NA) should be used to represent locations where there were no instances of these names

Code

allen |>
  filter(Year == "2000", State %in% c("PA", "CA")) |>
  pivot_wider(names_from = Name, values_from = Count, values_fill = 0) |>
  select(State, Alan, Allen, Allan) |>
  gt() |>
  tab_header(title = "Number of Alan, Allen, or Allan by State")

State	Alan	Allen	Allan
Number of Alan, Allen, or Allan by State
CA	579	176	131
PA	51	56	12

Convert your total counts to overall percents. That is, what was the percent breakdown between the three spellings in CA? What about in PA?

Code

# changed the decimals so they are percentages

allen |> 
  filter(Year == "2000", State %in% c("PA", "CA")) |>
  mutate(prop = Count / sum(Count)
         ) |>
  select(Sex, State, Name, prop) |>
  pivot_wider(names_from = Name, values_from = prop, values_fill = 0) |>
  gt() |>
  tab_header(title = "Percentage of Alan, Allen, or Allan by State") |>
  fmt_percent(columns = 3:5, decimals = 2)

Sex	State	Alan	Allen	Allan
Percentage of Alan, Allen, or Allan by State
M	CA	57.61%	17.51%	13.03%
M	PA	5.07%	5.57%	1.19%